# #需要修改的地方
# 1.地区code修改
# 2.判断节假日的配置问题
# 3.判断节假日代码修改问题
# 4.增加季节相关的特征问题
import pandas as pd
import os

# path = os.path.join(PROJECT_ROOT, "data\\database_config.csv")  # 文件路径
# #路径的修改
# curPath = os.path.abspath(os.path.dirname(__file__))
# rootPath = curPath[:curPath.find("aj\\")+len("aj\\")]
# dataPath = os.path.abspath(rootPath + 'data/database_config.csv')
# dataPath = os.path.abspath(rootPath + 'data/rest_holiday.csv')
# # data=pd.read_csv(path)
# # print(curPath)
# # print(rootPath)
# # print(dataPath)
# data=pd.read_csv(dataPath)
# print(data['rest_holiday'],type(data['rest_holiday']))
#仔细想想还是用电力预测的模式，简单，不然录入的时候，比较麻烦
# dataPath = os.path.abspath(rootPath + 'data/rest_workday.csv')
# data=pd.read_csv(path)
# print(curPath)
# print(rootPath)
# # print(dataPath)
# data=pd.read_csv(dataPath)
# print(data['rest_workday'],type(data['rest_workday']))
#地区倒是好改，所以我现在不想改
#节假日肯定要改的，改成电力预测的模式，还是改成别的呢。
#配置的话可以用俩文件，因为长度不一样嘛
#增加特征也好说。改就完事了
# import datetime
# result=[]
# date='20180815'
# date = pd.to_datetime(date)
# # result.append(datetime.date(date.year,date.month,date.day).isocalendar()[1])
# #哦
# result.append(datetime.date.isoweekday(datetime.datetime(date.year,date.month,date.day)))
# print(result)
import datetime
dataS=[]
predict_date = '20190907'
predict_date = datetime.datetime.strptime(predict_date, "%Y%m%d")

for i in range(1, 4):
    delta = datetime.timedelta(days=i)
    dataS.append(str(predict_date+delta)[:10])
# print(predict_date,type(predict_date))
print(dataS)